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import gradio as gr
import xyzservices.providers as xyz
from bokeh.plotting import figure
from bokeh.tile_providers import get_provider
from bokeh.models import ColumnDataSource, Whisker
from bokeh.plotting import figure
from bokeh.sampledata.autompg2 import autompg2 as df
from bokeh.sampledata.penguins import data
from bokeh.transform import factor_cmap, jitter, factor_mark
def get_plot(plot_type):
if plot_type == "map":
tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)
plot = figure(
x_range=(-2000000, 6000000),
y_range=(-1000000, 7000000),
x_axis_type="mercator",
y_axis_type="mercator",
)
plot.add_tile(tile_provider)
return plot
elif plot_type == "whisker":
classes = list(sorted(df["class"].unique()))
p = figure(
height=400,
x_range=classes,
background_fill_color="#efefef",
title="Car class vs HWY mpg with quintile ranges",
)
p.xgrid.grid_line_color = None
g = df.groupby("class")
upper = g.hwy.quantile(0.80)
lower = g.hwy.quantile(0.20)
source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))
error = Whisker(
base="base",
upper="upper",
lower="lower",
source=source,
level="annotation",
line_width=2,
)
error.upper_head.size = 20
error.lower_head.size = 20
p.add_layout(error)
p.circle(
jitter("class", 0.3, range=p.x_range),
"hwy",
source=df,
alpha=0.5,
size=13,
line_color="white",
color=factor_cmap("class", "Light6", classes),
)
return p
elif plot_type == "scatter":
SPECIES = sorted(data.species.unique())
MARKERS = ["hex", "circle_x", "triangle"]
p = figure(title="Penguin size", background_fill_color="#fafafa")
p.xaxis.axis_label = "Flipper Length (mm)"
p.yaxis.axis_label = "Body Mass (g)"
p.scatter(
"flipper_length_mm",
"body_mass_g",
source=data,
legend_group="species",
fill_alpha=0.4,
size=12,
marker=factor_mark("species", MARKERS, SPECIES),
color=factor_cmap("species", "Category10_3", SPECIES),
)
p.legend.location = "top_left"
p.legend.title = "Species"
return p
with gr.Blocks() as demo:
with gr.Row():
plot_type = gr.Radio(value="scatter", choices=["scatter", "whisker", "map"])
plot = gr.Plot()
plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])
demo.load(get_plot, inputs=[plot_type], outputs=[plot])
if __name__ == "__main__":
demo.launch() |